用扩散模型提升自动驾驶轨迹预测精度与可靠性
Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction
- 融合特征扩散模块与时空交互网络，建模复杂交通动态
- 在NGSIM、HighD等数据集上显著优于现有方法
- 适合需要高精度轨迹预测的自动驾驶系统研发
本文提出一种新型自动驾驶轨迹预测模型，结合特征扩散模块与时空交互网络，应对动态异构交通环境带来的挑战。模型通过引入不确定性估计和复杂智能体交互建模，提升轨迹预测的准确性和可靠性。在NGSIM、HighD和MoCAD等多个公开数据集上的大量实验表明，该模型显著优于现有最先进方法。结果证明其能有效捕捉交通场景中的潜在时空动态，在复杂环境下显著提高预测精度。所提模型在真实自动驾驶系统中具有广泛应用潜力。
原文摘要 · Abstract (English)
In this paper, we present a novel trajectory prediction model for autonomous driving, combining a Characterized Diffusion Module and a Spatial-Temporal Interaction Network to address the challenges posed by dynamic and heterogeneous traffic environments. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions. Through extensive experimentation on public datasets such as NGSIM, HighD, and MoCAD, our model significantly outperforms existing state-of-the-art methods. We demonstrate its ability to capture the underlying spatial-temporal dynamics of traffic scenarios and improve prediction precision, especially in complex environments. The proposed model showcases strong potential for application in real-world autonomous driving systems.
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